Category: Healthcare

Mining RFID Data: New Insights

by adminEducation / Healthcare / Industry / Logistics / Tourism

Only a small percentage of companies have adopted RFID technology in their supply chain and service operations so far. However, the commitment of leading organizations (such as the US Department of Defense) and companies (such as Walmart, JC Penney and PG) is expected to eventually spread the use of RFID, just as the barcode technology has gained acceptance over time.

A schedule-based system is a system that operates on or contains within a schedule of  events and breaks at particular time intervals. A recent study by an international team of researchers provides the answer: A framework that systematically produces insights for schedule-based system, answers the following questions:

  • What set of actions and what type of data mining methods can be applied for analyzing RFID data?
  • How can the method be integrated so that we can obtain actionable insights regarding the system and domain?
  • How do these methods apply, given RFID data from a schedule-based system?

Since RFID (Radio Frequency Identification) systems are gaining increasing importance in industry, a schedule-based system with RFID has been illustrated in this study. These systems are extensively encountered in a variety of domains, ranging from manufacturing to social event management.

The developed framework is general and is applicable to any system of this kind. The applicability of the developed framework is illustrated through a case study, where real world data is analyzed using the introduced framework.

The research question to be answered in this paper is the following: Given RFID data from a schedule-based system in any domain (such as social event management, manufacturing, healthcare, etc.) what set of actions (including the data cleaning steps) and computations, and what type of data analysis and data mining methods can be applied, so that one can obtain actionable insights regarding the system and the domain?

The paper gives multiple contributions: an analysis framework, including its mathematical representation, for mining RFID data coming from a schedule-based system. The different types of insights that can be obtained through the introduced framework were enumerated and the corresponding algorithms that are needed in the analysis framework were presented. Finally, authors demonstrated the applicability of the developed framework through a case study.

RFID technology has a great potential for facilitating and enhancing the management of social events, where humans interact with each other over time and across different locations. Therefore, the authors have chosen to present the application of RFID in the context of a social event, specifically a scientific conference. Case study also describes how this data can be used in real-time for informing conference attendees and illustrates how the system operates.

While applicable to any schedule-based supply chain, production, or service operation, the data used in particular in the study belongs to the domain of social event management and comes from a four-day medical conference. Each attendee of the conference was provided with a unique RFID tag and their entry and exit times to the single conference hall were recorded. The total number of attendees was 272.

RFID Systems Data Mining

In the context of practical use, these kinds of systems give many options. For example, in social event management, ubiquitous information systems can use this information to suggest new people for professional social networks – when two attendees are identified in the conference, as entering and exiting similar events, the conference mobile application can recommend them each other to add into LinkedIn and other professional social networks. In the context of warehousing, an example scenario where the information on similar-behaving entities can be used is the following: Let us assume pallets of similar-behaving products entering a warehouse. Chances are high that these similar-behaving products will also exit the warehouse at around the same time. Therefore, the warehouse management system (WMS) software can be programmed so as to allocate neighboring locations for these two pallets. This way, these products can be put away and picked on the same route, saving time and cost.

The importance of RFID systems is ever increasing, and they find applications in a very wide range of domains. Authors consider there is a good foundation for further research, such as: extending the framework from the temporal domain to the spatiotemporal domain, by extending it to handle multiple locations. One of the important challenges in industrial applications is the challenge of big data. A possible future research can involve the development of the framework to accommodate for big data applications. The novelty of the research is the introduction of a data mining framework for the first time for this type of a system while the importance of the research lies in the fact it can be applied generally in a wide range of domains.

In-depth analysis with all the research steps of the study can be found in this link: https://ertekprojects.com/gurdal-ertek-publications/framework-mining-rfid-data-schedule-based-systems/

A New Data Mining Approach For Healthcare Center Operations

by adminData Science / Healthcare

With constant growth of world population, demand for healthcare services is growing as well, increasing the healthcare costs by 2.4-7.5% per year until 2020 in various countries. Healthcare market in the United Arab Emirates (U.A.E.) is expected to grow 12.7% per year, until 2020 and will possibly grow even further due to development of medical tourism, an economic priority for U.A.E.

Analytical tools, namely, data mining and optimization can contribute to a large degree in achieving operational efficiencies.

Dr. Gurdal Ertek and Dr. Salam Abdallah from Abu Dhabi University, together with Dr. Mohsin Malik from The University of Melbourne, have developed a new data mining approach for healthcare operations. The approach has been applied using real-world data from one of the largest public hospitals in the U.A.E, demonstrating its applicability and benefits.

Among other key points, the study quantifies and analyzes the timing of patient arrivals in order to improve the patient admission process step through elimination of wasted time. It is crucial to point out the main performance measure in the study was lateness and the association of lateness with various factors.

The importance of this segment of the study is immense, as there are no academic studies on predicting lateness as a function of schedule day and time. There is, also, a significant gap of knowledge regarding the understanding of the lateness and the factors underlying lateness in healthcare centers. Dr. Ertek, Dr. Abdallah and Dr. Malik focused on the way patient arrivals into healthcare centers can be analyzed in order to come up with insights into lateness. They researched about factors associated with lateness and developed suggestions on how patient appointments can be scheduled to minimize this issue.

Study also presents an appointment scheduling approach, based on the fact that that lateness is dependent on the scheduled time, rather than being independent of it. One of the key contributions of the research study is a subsequent optimization model for scheduling appointments that may improve operational efficiency.

Some of the essential insights of this data mining approach are precious and include presentation of hidden patterns that generate information about patient arrival patterns, identification of independent variables that affect lateness, and determination of form and parameters of the best regression function. The three experts have concluded that the approach they developed can serve as an engine for determining optimal appointment day and times for healthcare centers.

Compared to other closely-related researches, this one is at least 10-fold larger, providing, so far, the most relevant and most detailed information regarding this subject. The study can be downloaded online and the approach can be applied by healthcare practitioners to analyze data from their own organizations.